The study utilizes a large collection of fluorescently marked Ds-GFP insertional mutations in haploid maize pollen to link gene disruptions with quantitative fitness effects measured as transmission deviations. By integrating genome-derived features (e.g., codon usage) and expression profiling into interpretable machine learning models, they achieve high predictive performance (auROC >90%) for genes influencing pollen fitness, highlighting expression specificity as a key predictor.

The study examines how ectopic accumulation of methionine in Arabidopsis thaliana leaves, driven by a deregulated AtCGS transgene under a seed‑specific promoter, reshapes metabolism, gene expression, and DNA methylation. High‑methionine lines exhibit increased amino acids and sugars, activation of stress‑hormone pathways, and reduced expression of DNA methyltransferases, while low‑methionine lines show heightened non‑CG methylation without major transcriptional changes. Integrated transcriptomic and methylomic analyses reveal a feedback loop linking sulfur‑carbon metabolism, stress adaptation, and epigenetic regulation.

The study benchmarked over 20 web‑based gRNA on‑target efficiency prediction tools against an experimental plant CRISPR editing dataset, finding several machine‑learning based tools whose scores strongly correlated with observed InDel frequencies. Additionally, the performance of popular platforms such as CRISPOR and CRISPR‑P was assessed, offering guidance for improved gRNA design in plant genome editing.

The study evaluates the use of single-cell RNA sequencing (scRNA-seq) data to predict plant metabolic pathway genes (MPGs) in Arabidopsis thaliana, comparing five multi-label machine‑learning algorithms against traditional bulk RNA‑seq approaches. scRNA‑seq generated co‑expression networks that, while different, yielded significantly higher MPG classification accuracy, especially when data were split by genetic background or tissue type, and deep learning outperformed classical methods. The authors conclude that scRNA‑seq offers superior predictive power and should be incorporated into future MPG discovery pipelines.

The study integrated weekly morphophysiological measurements with high-density genotyping-by-sequencing data and a machine‑learning pipeline to dissect flowering time variation in diverse Cannabis sativa landraces. By applying mutual information, recursive feature elimination, random forest, and support vector machine classifiers to over 234,000 combined genetic, phenotypic, and environmental features, the authors identified 53 key markers that classify early, medium, and late flowering types with 96.6% accuracy. Notable loci, including CsFT3 and CsCFL1, were highlighted as promising targets for breeding and smart‑crop strategies.

The study used single‑cell transcriptomics to compare Arabidopsis thaliana leaf cell responses during pattern‑triggered and effector‑triggered immunity, revealing that core defense modules are broadly shared but differ in timing, intensity, and cell‑type specific receptor dynamics. Distinct mesophyll subpopulations showed divergent resilience patterns, and gene regulatory network analysis identified WRKY‑regulated and salicylic‑acid biosynthesis modules, with the cue1-6 mutant confirming robustness of core immune responses while exposing cryptic sucrose‑responsive pathways.

The study used extensive gravimetric load‑cell and ambient sensor data collected over seven years from hundreds of greenhouse-grown crops to train machine‑learning models for predicting daily whole‑plant transpiration. Random Forest and XGBoost achieved the highest accuracy (R² up to 0.89), with ambient temperature identified as the dominant driver. These results highlight the promise of ML‑based tools for precise agricultural water management.

The study used high-resolution chemical imaging to map cell-type specific metabolic changes in plant roots inoculated with a nine-strain endophyte consortium under drought, revealing that endophytes differentially alter root metabolomes across spatial domains. Machine learning identified metabolites and exudates predictive of drought and endophyte treatment, and correlation analyses showed dynamic endophyte–metabolite relationships under stress.

The study presents a multiplexed technique that applies both single‑cell RNA sequencing (scRNA‑seq) and single‑cell mass spectrometry metabolomics (scMS) to the same plant cell, enabling direct, cell‑by‑cell correlation of gene expression with metabolite levels. This integrated approach uncovers qualitative and quantitative relationships between biosynthetic gene activity and metabolite accumulation, advancing understanding of complex plant natural product pathways.

The study generated high-resolution single-cell and single-nucleus RNA‑seq transcriptome atlases for two independently evolved C4 dicots, Gynandropsis gynandra (NAD-ME) and Flaveria bidentis (NADP-ME), enabling detailed comparison of leaf cell types. Bundle sheath cells in both species displayed a gene‑expression profile resembling C3 mesophyll cells and shared a conserved set of C2H2 and DOF transcription factors, revealing common regulatory features of C4 evolution.